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Record W2271082682 · doi:10.1111/mms.12291

Behavior and activity budgets of wild breeding polar bears (<i>Ursus maritimus</i>)

2015· article· en· W2271082682 on OpenAlexaffabout
Ian Stirling, Cheryl Spencer, Dennis Andriashek

Bibliographic record

VenueMarine Mammal Science · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsUrsus maritimusMatingSeasonal breederBiologyZoologyBayEcologySexual selectionReproductionGeographyArctic

Abstract

fetched live from OpenAlex

Abstract We quantify the first complete description of breeding behavior and activity budgets of an undisturbed pair of adult polar bears, observed 24 h/d for 13 d from 2 to 15 May 1997, at Radstock Bay, Devon Island, Nunavut, Canada. The male herded the female to an area of 1–2 km2, where we observed them throughout the observation period. All behaviors were documented from when the adult female and her 2.5‐yr‐old cub were first observed being followed by an adult male, through separation of the cub from its mother, a week of intense interactions preceding several days with copulation, after which they parted. They mated for 51, 86, 66, and 150 min on 9–10, 12, 13, and 14 May, respectively, and parted on 14–15 May. The male deterred three challengers. The peak breeding season for polar bears runs from early April through mid‐May, although additional mating behavior has been documented in June. Timing of mating and duration of copulations in the wild were similar to reports from zoos. Induced ovulation, male intrasexual competition, female fitness, the mating system, and potential consequences of climate warming are discussed with insights made possible by documentation of the reproductive behavior of wild polar bears.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.026
GPT teacher head0.254
Teacher spread0.229 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations93
Published2015
Admission routes2
Has abstractyes

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